class_docstr: The autoencoder-based multivariate time series anomaly detector.
  This detector utilizes an autoencoder to infer the correlations between
  different time series and estimate the joint distribution of the variables
  for anomaly detection.
  
  - paper: `Pierre Baldi. Autoencoders, Unsupervised Learning, and Deep Architectures. 2012.
    <https://proceedings.mlr.press/v27/baldi12a.html>`_
code: def require_even_sampling(self) -> bool:
      return False
  
imports: from typing import Sequence
  import numpy
  import pandas
  from merlion.models.base import NormalizingConfig
  from merlion.models.anomaly.base import DetectorBase, DetectorConfig
  from merlion.post_process.threshold import AggregateAlarms
  from merlion.utils.misc import ProgressBar, initializer
  from merlion.models.utils.rolling_window_dataset import RollingWindowDataset
label_desc: Use image node_id 1 for calling the AutoEncoder obj's underlying member method code with example usage: obj.require_even_sampling() and returns: False